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	# Dual Attention Network for Scene Segmentation

	## Introduction

	[ALGORITHM]

	```latex
	@article{fu2018dual,
	title={Dual Attention Network for Scene Segmentation},
	author={Jun Fu, Jing Liu, Haijie Tian, Yong Li, Yongjun Bao, Zhiwei Fang,and Hanqing Lu},
	booktitle={The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
	year={2019}
	}
	```

	## Results and models

	### Cityscapes

	\| Method \| Backbone \| Crop Size \| Lr schd \| Mem (GB) \| Inf time (fps) \| mIoU \| mIoU(ms+flip) \| download \|
	\|--------\|----------\|-----------\|--------:\|----------\|----------------\|------:\|---------------\|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|
	\| DANet \| R-50-D8 \| 512x1024 \| 40000 \| 7.4 \| 2.66 \| 78.74 \| - \| [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x1024_40k_cityscapes/danet_r50-d8_512x1024_40k_cityscapes_20200605_191324-c0dbfa5f.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x1024_40k_cityscapes/danet_r50-d8_512x1024_40k_cityscapes_20200605_191324.log.json) \|
	\| DANet \| R-101-D8 \| 512x1024 \| 40000 \| 10.9 \| 1.99 \| 80.52 \| - \| [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x1024_40k_cityscapes/danet_r101-d8_512x1024_40k_cityscapes_20200605_200831-c57a7157.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x1024_40k_cityscapes/danet_r101-d8_512x1024_40k_cityscapes_20200605_200831.log.json) \|
	\| DANet \| R-50-D8 \| 769x769 \| 40000 \| 8.8 \| 1.56 \| 78.88 \| 80.62 \| [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_769x769_40k_cityscapes/danet_r50-d8_769x769_40k_cityscapes_20200530_025703-76681c60.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_769x769_40k_cityscapes/danet_r50-d8_769x769_40k_cityscapes_20200530_025703.log.json) \|
	\| DANet \| R-101-D8 \| 769x769 \| 40000 \| 12.8 \| 1.07 \| 79.88 \| 81.47 \| [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_769x769_40k_cityscapes/danet_r101-d8_769x769_40k_cityscapes_20200530_025717-dcb7fd4e.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_769x769_40k_cityscapes/danet_r101-d8_769x769_40k_cityscapes_20200530_025717.log.json) \|
	\| DANet \| R-50-D8 \| 512x1024 \| 80000 \| - \| - \| 79.34 \| - \| [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x1024_80k_cityscapes/danet_r50-d8_512x1024_80k_cityscapes_20200607_133029-2bfa2293.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x1024_80k_cityscapes/danet_r50-d8_512x1024_80k_cityscapes_20200607_133029.log.json) \|
	\| DANet \| R-101-D8 \| 512x1024 \| 80000 \| - \| - \| 80.41 \| - \| [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x1024_80k_cityscapes/danet_r101-d8_512x1024_80k_cityscapes_20200607_132918-955e6350.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x1024_80k_cityscapes/danet_r101-d8_512x1024_80k_cityscapes_20200607_132918.log.json) \|
	\| DANet \| R-50-D8 \| 769x769 \| 80000 \| - \| - \| 79.27 \| 80.96 \| [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_769x769_80k_cityscapes/danet_r50-d8_769x769_80k_cityscapes_20200607_132954-495689b4.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_769x769_80k_cityscapes/danet_r50-d8_769x769_80k_cityscapes_20200607_132954.log.json) \|
	\| DANet \| R-101-D8 \| 769x769 \| 80000 \| - \| - \| 80.47 \| 82.02 \| [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_769x769_80k_cityscapes/danet_r101-d8_769x769_80k_cityscapes_20200607_132918-f3a929e7.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_769x769_80k_cityscapes/danet_r101-d8_769x769_80k_cityscapes_20200607_132918.log.json) \|

	### ADE20K

	\| Method \| Backbone \| Crop Size \| Lr schd \| Mem (GB) \| Inf time (fps) \| mIoU \| mIoU(ms+flip) \| download \|
	\|--------\|----------\|-----------\|--------:\|----------\|----------------\|------:\|--------------:\|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|
	\| DANet \| R-50-D8 \| 512x512 \| 80000 \| 11.5 \| 21.20 \| 41.66 \| 42.90 \| [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x512_80k_ade20k/danet_r50-d8_512x512_80k_ade20k_20200615_015125-edb18e08.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x512_80k_ade20k/danet_r50-d8_512x512_80k_ade20k_20200615_015125.log.json) \|
	\| DANet \| R-101-D8 \| 512x512 \| 80000 \| 15 \| 14.18 \| 43.64 \| 45.19 \| [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x512_80k_ade20k/danet_r101-d8_512x512_80k_ade20k_20200615_015126-d0357c73.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x512_80k_ade20k/danet_r101-d8_512x512_80k_ade20k_20200615_015126.log.json) \|
	\| DANet \| R-50-D8 \| 512x512 \| 160000 \| - \| - \| 42.45 \| 43.25 \| [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x512_160k_ade20k/danet_r50-d8_512x512_160k_ade20k_20200616_082340-9cb35dcd.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x512_160k_ade20k/danet_r50-d8_512x512_160k_ade20k_20200616_082340.log.json) \|
	\| DANet \| R-101-D8 \| 512x512 \| 160000 \| - \| - \| 44.17 \| 45.02 \| [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x512_160k_ade20k/danet_r101-d8_512x512_160k_ade20k_20200616_082348-23bf12f9.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x512_160k_ade20k/danet_r101-d8_512x512_160k_ade20k_20200616_082348.log.json) \|

	### Pascal VOC 2012 + Aug

	\| Method \| Backbone \| Crop Size \| Lr schd \| Mem (GB) \| Inf time (fps) \| mIoU \| mIoU(ms+flip) \| download \|
	\|--------\|----------\|-----------\|--------:\|----------\|----------------\|------:\|--------------:\|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|
	\| DANet \| R-50-D8 \| 512x512 \| 20000 \| 6.5 \| 20.94 \| 74.45 \| 75.69 \| [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x512_20k_voc12aug/danet_r50-d8_512x512_20k_voc12aug_20200618_070026-9e9e3ab3.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x512_20k_voc12aug/danet_r50-d8_512x512_20k_voc12aug_20200618_070026.log.json) \|
	\| DANet \| R-101-D8 \| 512x512 \| 20000 \| 9.9 \| 13.76 \| 76.02 \| 77.23 \| [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x512_20k_voc12aug/danet_r101-d8_512x512_20k_voc12aug_20200618_070026-d48d23b2.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x512_20k_voc12aug/danet_r101-d8_512x512_20k_voc12aug_20200618_070026.log.json) \|
	\| DANet \| R-50-D8 \| 512x512 \| 40000 \| - \| - \| 76.37 \| 77.29 \| [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x512_40k_voc12aug/danet_r50-d8_512x512_40k_voc12aug_20200613_235526-426e3a64.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x512_40k_voc12aug/danet_r50-d8_512x512_40k_voc12aug_20200613_235526.log.json) \|
	\| DANet \| R-101-D8 \| 512x512 \| 40000 \| - \| - \| 76.51 \| 77.32 \| [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x512_40k_voc12aug/danet_r101-d8_512x512_40k_voc12aug_20200613_223031-788e232a.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x512_40k_voc12aug/danet_r101-d8_512x512_40k_voc12aug_20200613_223031.log.json) \|

	# Dual Attention Network for Scene Segmentation

	## Introduction

	[ALGORITHM]

	```latex
	@article{fu2018dual,
	title={Dual Attention Network for Scene Segmentation},
	author={Jun Fu, Jing Liu, Haijie Tian, Yong Li, Yongjun Bao, Zhiwei Fang,and Hanqing Lu},
	booktitle={The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
	year={2019}
	}
	```

	## Results and models

	### Cityscapes

	\| Method \| Backbone \| Crop Size \| Lr schd \| Mem (GB) \| Inf time (fps) \| mIoU \| mIoU(ms+flip) \| download \|
	\|--------\|----------\|-----------\|--------:\|----------\|----------------\|------:\|---------------\|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|
	\| DANet \| R-50-D8 \| 512x1024 \| 40000 \| 7.4 \| 2.66 \| 78.74 \| - \| [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x1024_40k_cityscapes/danet_r50-d8_512x1024_40k_cityscapes_20200605_191324-c0dbfa5f.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x1024_40k_cityscapes/danet_r50-d8_512x1024_40k_cityscapes_20200605_191324.log.json) \|
	\| DANet \| R-101-D8 \| 512x1024 \| 40000 \| 10.9 \| 1.99 \| 80.52 \| - \| [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x1024_40k_cityscapes/danet_r101-d8_512x1024_40k_cityscapes_20200605_200831-c57a7157.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x1024_40k_cityscapes/danet_r101-d8_512x1024_40k_cityscapes_20200605_200831.log.json) \|
	\| DANet \| R-50-D8 \| 769x769 \| 40000 \| 8.8 \| 1.56 \| 78.88 \| 80.62 \| [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_769x769_40k_cityscapes/danet_r50-d8_769x769_40k_cityscapes_20200530_025703-76681c60.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_769x769_40k_cityscapes/danet_r50-d8_769x769_40k_cityscapes_20200530_025703.log.json) \|
	\| DANet \| R-101-D8 \| 769x769 \| 40000 \| 12.8 \| 1.07 \| 79.88 \| 81.47 \| [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_769x769_40k_cityscapes/danet_r101-d8_769x769_40k_cityscapes_20200530_025717-dcb7fd4e.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_769x769_40k_cityscapes/danet_r101-d8_769x769_40k_cityscapes_20200530_025717.log.json) \|
	\| DANet \| R-50-D8 \| 512x1024 \| 80000 \| - \| - \| 79.34 \| - \| [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x1024_80k_cityscapes/danet_r50-d8_512x1024_80k_cityscapes_20200607_133029-2bfa2293.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x1024_80k_cityscapes/danet_r50-d8_512x1024_80k_cityscapes_20200607_133029.log.json) \|
	\| DANet \| R-101-D8 \| 512x1024 \| 80000 \| - \| - \| 80.41 \| - \| [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x1024_80k_cityscapes/danet_r101-d8_512x1024_80k_cityscapes_20200607_132918-955e6350.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x1024_80k_cityscapes/danet_r101-d8_512x1024_80k_cityscapes_20200607_132918.log.json) \|
	\| DANet \| R-50-D8 \| 769x769 \| 80000 \| - \| - \| 79.27 \| 80.96 \| [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_769x769_80k_cityscapes/danet_r50-d8_769x769_80k_cityscapes_20200607_132954-495689b4.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_769x769_80k_cityscapes/danet_r50-d8_769x769_80k_cityscapes_20200607_132954.log.json) \|
	\| DANet \| R-101-D8 \| 769x769 \| 80000 \| - \| - \| 80.47 \| 82.02 \| [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_769x769_80k_cityscapes/danet_r101-d8_769x769_80k_cityscapes_20200607_132918-f3a929e7.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_769x769_80k_cityscapes/danet_r101-d8_769x769_80k_cityscapes_20200607_132918.log.json) \|

	### ADE20K

	\| Method \| Backbone \| Crop Size \| Lr schd \| Mem (GB) \| Inf time (fps) \| mIoU \| mIoU(ms+flip) \| download \|
	\|--------\|----------\|-----------\|--------:\|----------\|----------------\|------:\|--------------:\|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|
	\| DANet \| R-50-D8 \| 512x512 \| 80000 \| 11.5 \| 21.20 \| 41.66 \| 42.90 \| [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x512_80k_ade20k/danet_r50-d8_512x512_80k_ade20k_20200615_015125-edb18e08.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x512_80k_ade20k/danet_r50-d8_512x512_80k_ade20k_20200615_015125.log.json) \|
	\| DANet \| R-101-D8 \| 512x512 \| 80000 \| 15 \| 14.18 \| 43.64 \| 45.19 \| [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x512_80k_ade20k/danet_r101-d8_512x512_80k_ade20k_20200615_015126-d0357c73.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x512_80k_ade20k/danet_r101-d8_512x512_80k_ade20k_20200615_015126.log.json) \|
	\| DANet \| R-50-D8 \| 512x512 \| 160000 \| - \| - \| 42.45 \| 43.25 \| [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x512_160k_ade20k/danet_r50-d8_512x512_160k_ade20k_20200616_082340-9cb35dcd.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x512_160k_ade20k/danet_r50-d8_512x512_160k_ade20k_20200616_082340.log.json) \|
	\| DANet \| R-101-D8 \| 512x512 \| 160000 \| - \| - \| 44.17 \| 45.02 \| [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x512_160k_ade20k/danet_r101-d8_512x512_160k_ade20k_20200616_082348-23bf12f9.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x512_160k_ade20k/danet_r101-d8_512x512_160k_ade20k_20200616_082348.log.json) \|

	### Pascal VOC 2012 + Aug

	\| Method \| Backbone \| Crop Size \| Lr schd \| Mem (GB) \| Inf time (fps) \| mIoU \| mIoU(ms+flip) \| download \|
	\|--------\|----------\|-----------\|--------:\|----------\|----------------\|------:\|--------------:\|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|
	\| DANet \| R-50-D8 \| 512x512 \| 20000 \| 6.5 \| 20.94 \| 74.45 \| 75.69 \| [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x512_20k_voc12aug/danet_r50-d8_512x512_20k_voc12aug_20200618_070026-9e9e3ab3.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x512_20k_voc12aug/danet_r50-d8_512x512_20k_voc12aug_20200618_070026.log.json) \|
	\| DANet \| R-101-D8 \| 512x512 \| 20000 \| 9.9 \| 13.76 \| 76.02 \| 77.23 \| [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x512_20k_voc12aug/danet_r101-d8_512x512_20k_voc12aug_20200618_070026-d48d23b2.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x512_20k_voc12aug/danet_r101-d8_512x512_20k_voc12aug_20200618_070026.log.json) \|
	\| DANet \| R-50-D8 \| 512x512 \| 40000 \| - \| - \| 76.37 \| 77.29 \| [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x512_40k_voc12aug/danet_r50-d8_512x512_40k_voc12aug_20200613_235526-426e3a64.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x512_40k_voc12aug/danet_r50-d8_512x512_40k_voc12aug_20200613_235526.log.json) \|
	\| DANet \| R-101-D8 \| 512x512 \| 40000 \| - \| - \| 76.51 \| 77.32 \| [model](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x512_40k_voc12aug/danet_r101-d8_512x512_40k_voc12aug_20200613_223031-788e232a.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x512_40k_voc12aug/danet_r101-d8_512x512_40k_voc12aug_20200613_223031.log.json) \|